A Coordinated and Resilient Formation Strategy Based on Hierarchical Reorganization
Multi-leader formations offer superior flexibility and adaptability compared to single-leader configurations. However, the failure of even a single leader can pose significant risks to the overall success of hierarchical formations. Although existing strategies to address leader failures often rely on dynamic re-election mechanisms, these approaches are primarily tailored to single-leader configurations. To overcome these limitations, this paper presents a resilient formation strategy based on hierarchical reorganization. The central concept is to endow the formation with fail-tolerance through seamless leadership transitions while preserving overall agility. Specifically, we propose a comprehensive fail-tolerant leadership evaluation algorithm capable of selecting the most agile leadership configuration while maintaining formation safety. Recognizing that distributed evaluations may yield inconsistent leader selections, we integrate a Raft-based configuration consensus mechanism to achieve distributed agreement during hierarchical reorganization. Additionally, to guarantee the smooth execution of the reorganization process, a synchronous state updating strategy is adopted to mitigate communication delays, thereby facilitating seamless reconfiguration. We conducted extensive simulations and real-world experiments. Experiments results across multiple scenarios demonstrate that the proposed strategy swiftly identifies malfunctioning leaders, mitigates their adverse effects through hierarchical reorganization, and improves the mission success rate of a 7-UAV formation from 28.6% to 85.7%. Overall, our findings show that the proposed approach not only addresses individual agent failures but also significantly enhances the formation's stability and robustness.
Authors
- Wei Dong (ORCID: https://orcid.org/0000-0003-2640-1585)
- Yuzhu Li (ORCID: https://orcid.org/0000-0001-8065-2364)
Institutions
- Shanghai Jiao Tong University (CN)
Publication Details
- Journal
- IEEE Robotics and Automation Letters
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1109/lra.2025.3604698
- Primary Topic
- Complex Systems and Decision Making
- Type
- article
- Field-Weighted Citation Impact
- 0.00